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ffw_sg2_rev1,
instruction "wave using the right hand"). The policy conditions on joint state only — no cameras.| Inputs | observation.state (22): arm_l ×7, gripper_l, arm_r ×7, gripper_r, head ×2, lift, cmd_vel linear_x / linear_y / angular_z |
| Outputs | action (22), same layout, published to /leader/*/joint_trajectory and /cmd_vel |
| Chunking | n_obs_steps=1, horizon=32, n_action_steps=16 at 15 Hz (= 2.1 s predicted, 1.07 s executed per chunk) |
| Inference | DDPM, num_inference_steps=10 (≈ 70 ms on an A5000; 100 steps gives the same accuracy at ~650 ms) |
| Training | 30 k steps, batch 64, lr 1e-4 cosine, seed 1000; final loss 1e-3; offline 16-step open-loop MAE 0.002 rad |
START — the policy has never seen states far from it, and in
MuJoCo rollouts it waved reliably from starts up to ~0.24 rad (per joint) away from the nearest demo start.
Head and lift were fixed during collection, so use exactly those values. Base velocity dims must read ~0.| joint | mean [rad] | std | range over demos |
|---|---|---|---|
arm_l_joint1 | -0.138 | 0.061 | [-0.222, -0.036] |
arm_l_joint2 | +0.168 | 0.027 | [+0.114, +0.203] |
arm_l_joint3 | -0.081 | 0.052 | [-0.180, -0.004] |
arm_l_joint4 | -1.452 | 0.112 | [-1.669, -1.298] |
arm_l_joint5 | +0.198 | 0.054 | [+0.104, +0.281] |
arm_l_joint6 | +0.079 | 0.096 | [-0.126, +0.233] |
arm_l_joint7 | +0.018 | 0.034 | [-0.037, +0.109] |
gripper_l_joint1 | +0.254 | 0.063 | [+0.163, +0.345] |
arm_r_joint1 | -0.161 | 0.079 | [-0.304, -0.045] |
arm_r_joint2 | -0.069 | 0.024 | [-0.106, -0.024] |
arm_r_joint3 | +0.025 | 0.056 | [-0.075, +0.142] |
arm_r_joint4 | -1.402 | 0.125 | [-1.627, -1.232] |
arm_r_joint5 | -0.026 | 0.047 | [-0.115, +0.053] |
arm_r_joint6 | -0.012 | 0.093 | [-0.206, +0.113] |
arm_r_joint7 | -0.015 | 0.119 | [-0.252, +0.130] |
gripper_r_joint1 | +0.258 | 0.161 | [+0.054, +0.503] |
head_joint1 | -0.204 | 0.000 | [-0.204, -0.204] |
head_joint2 | -0.292 | 0.000 | [-0.292, -0.292] |
lift_joint | -0.000 | 0.001 | [-0.002, +0.000] |
linear_x | -0.000 | 0.000 | [-0.000, +0.000] |
linear_y | +0.000 | 0.000 | [-0.000, +0.000] |
angular_z | +0.000 | 0.000 | [-0.001, +0.000] |
initial_state.json (initial_state_mean is the vector to command,
in joint_names order; final_state_mean is where the demos end, i.e. the rest pose the policy returns to).inference_hz = 15 (default) and control_hz = 100 in the UI; ActionChunkProcessor spaces the chunk's
16 steps at 1/inference_hz, so 15 Hz must match the dataset fps this policy was trained at.(B, D) state per request. Both are handled by cyclo_brain/policy/lerobot/lerobot_engine/diffusion_compat.py
(loaded automatically by the engine's loading.py / prediction.py) — the policy container needs that version of the
bind-mounted lerobot_engine/. Loading in plain LeRobot: call diffusion_compat.allow_state_only_diffusion() first, then
DiffusionPolicy.from_pretrained(...), and add the time axis with expand_obs_time_dim(batch, 1) before predict_action_chunk.lerobot-mujoco-tutorial FFW SG2 model (right-arm joint range 88 % of the demonstrations',
left arm quiet, 0.012 rad servo tracking error).